The NSA’s SKYNET program may be killing thousands of innocent people

"Completely bullshit"

The problem, Ball told Ars, is how the NSA trains the algorithm with ground truths.

The NSA evaluates the SKYNET program using a subset of 100,000 randomly selected people (identified by their MSIDN/MSI pairs of their mobile phones), and a a known group of seven terrorists. The NSA then trained the learning algorithm by feeding it six of the terrorists and tasking SKYNET to find the seventh. This data provides the percentages for false positives in the slide above.

"First, there are very few 'known terrorists' to use to train and test the model," Ball said. "If they are using the same records to train the model as they are using to test the model, their assessment of the fit is completely bullshit. The usual practice is to hold some of the data out of the training process so that the test includes records the model has never seen before. Without this step, their classification fit assessment is ridiculously optimistic."

The reason is that the 100,000 citizens were selected at random, while the seven terrorists are from a known cluster. Under the random selection of a tiny subset of less than 0.1 percent of the total population, the density of the social graph of the citizens is massively reduced, while the "terrorist" cluster remains strongly interconnected. Scientifically-sound statistical analysis would have required the NSA to mix the terrorists into the population set before random selection of a subset—but this is not practical due to their tiny number.

This may sound like a mere academic problem, but, Ball said, is in fact highly damaging to the quality of the results, and thus ultimately to the accuracy of the classification and assassination of people as "terrorists." A quality evaluation is especially important in this case, as the random forest method is known to overfit its training sets, producing results that are overly optimistic. The NSA's analysis thus does not provide a good indicator of the quality of the method.

Enlarge/ A false positive rate of 0.18 percent across 55 million people would mean 99,000 innocents mislabelled as "terrorists"

If 50 percent of the false negatives (actual "terrorists") are allowed to survive, the NSA's false positive rate of 0.18 percent would still mean thousands of innocents misclassified as "terrorists" and potentially killed. Even the NSA's most optimistic result, the 0.008 percent false positive rate, would still result in many innocent people dying.

"On the slide with the false positive rates, note the final line that says '+ Anchory Selectors,'" Danezis told Ars. "This is key, and the figures are unreported... if you apply a classifier with a false-positive rate of 0.18 percent to a population of 55 million you are indeed likely to kill thousands of innocent people. [0.18 percent of 55 million = 99,000]. If however you apply it to a population where you already expect a very high prevalence of 'terrorism'—because for example they are in the two-hop neighbourhood of a number of people of interest—then the prior goes up and you will kill fewer innocent people."

Besides the obvious objection of how many innocent people it is ever acceptable to kill, this also assumes there are a lot of terrorists to identify. "We know that the 'true terrorist' proportion of the full population is very small," Ball pointed out. "As Cory [Doctorow] says, if this were not true, we would all be dead already. Therefore a small false positive rate will lead to misidentification of lots of people as terrorists."

"The larger point," Ball added, "is that the model will totally overlook 'true terrorists' who are statistically different from the 'true terrorists' used to train the model."

In most cases, a failure rate of 0.008% would be great...

The 0.008 percent false positive rate would be remarkably low for traditional business applications. This kind of rate is acceptable where the consequences are displaying an ad to the wrong person, or charging someone a premium price by accident. However, even 0.008 percent of the Pakistani population still corresponds to 15,000 people potentially being misclassified as "terrorists" and targeted by the military—not to mention innocent bystanders or first responders who happen to get in the way.

Security guru Bruce Schneier agreed. "Government uses of big data are inherently different from corporate uses," he told Ars. "The accuracy requirements mean that the same technology doesn't work. If Google makes a mistake, people see an ad for a car they don't want to buy. If the government makes a mistake, they kill innocents."

Further Reading

Killing civilians is forbidden by the Geneva Convention, to which the United States is a signatory. Many facts about the SKYNET program remain unknown, however. For instance, is SKYNET a closed loop system, or do analysts review each mobile phone user's profile before condemning them to death based on metadata? Are efforts made to capture these suspected "terrorists" and put them on trial? How can the US government be sure it is not killing innocent people, given the apparent flaws in the machine learning algorithm on which that kill list is based?

"On whether the use of SKYNET is a war crime, I defer to lawyers," Ball said. "It's bad science, that's for damn sure, because classification is inherently probabilistic. If you're going to condemn someone to death, usually we have a 'beyond a reasonable doubt' standard, which is not at all the case when you're talking about people with 'probable terrorist' scores anywhere near the threshold. And that's assuming that the classifier works in the first place, which I doubt because there simply aren't enough positive cases of known terrorists for the random forest to get a good model of them."

The leaked NSA slide decks offer strong evidence that thousands of innocent people are being labelled as terrorists; what happens after that, we don't know. We don't have the full picture, nor is the NSA likely to fill in the gaps for us. (We repeatedly sought comment from the NSA for this story, but at the time of publishing it had not responded.)

Algorithms increasingly rule our lives. It's a small step from applying SKYNET logic to look for "terrorists" in Pakistan to applying the same logic domestically to look for "drug dealers" or "protesters" or just people who disagree with the state. Killing people "based on metadata," as Hayden said, is easy to ignore when it happens far away in a foreign land. But what happens when SKYNET gets turned on us—assuming it hasn't been already?

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Christian Grothoff leads the Décentralisé research team at Inria, a French institute for applied computer science and mathematics research. He earned his PhD in computer science from UCLA, an MS in computer science from Purdue University, and a diploma in mathematics from the University of Wuppertal. He is also a freelance journalist reporting on technology and national security.

J.M. Porup is a freelance cybersecurity reporter who lives in Toronto. When he dies his epitaph will simply read "assume breach." You can find him on Twitter at @toholdaquill.